Updated: 2020-08-10 10:56:44 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
South Dakota 1.31 9442 111
Virginia 1.19 79814 994
Indiana 1.18 76026 1029
Vermont 1.18 1460 5
North Dakota 1.17 7587 144
Illinois 1.14 194844 1875
West Virginia 1.11 7721 137
Arkansas 1.10 47840 826
Idaho 1.10 24977 512
Kentucky 1.08 36573 669
Georgia 1.07 197808 3498
Minnesota 1.07 60767 753
Iowa 1.06 48979 499
Wisconsin 1.05 60894 901
Oregon 1.03 21312 328
Alabama 1.01 101566 1590
New Hampshire 1.01 6843 29
New York 1.01 425684 666
Montana 1.00 4920 115
Texas 1.00 511264 8044
North Carolina 0.99 136537 1644
Tennessee 0.99 119653 1990
Ohio 0.98 101100 1170
Utah 0.98 44260 438
California 0.97 562860 7068
Kansas 0.97 31351 381
Massachusetts 0.97 120776 384
Washington 0.97 65295 720
Michigan 0.96 96722 687
Colorado 0.95 50932 451
Delaware 0.94 15359 84
Oklahoma 0.94 43857 831
South Carolina 0.94 100765 1295
Mississippi 0.93 67586 1022
Nebraska 0.93 28398 258
Nevada 0.93 56465 892
New Jersey 0.93 186001 350
Florida 0.92 532493 7130
Louisiana 0.92 131391 1588
Maryland 0.91 96076 762
Pennsylvania 0.91 123506 739
Missouri 0.89 53039 928
Rhode Island 0.88 17965 80
Wyoming 0.87 3058 35
New Mexico 0.84 22352 190
Maine 0.80 4058 14
Arizona 0.70 187746 1296
Connecticut 0.47 50171 50

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 24 1 1.8 122 250 5
King WA 1 2 1.0 16628 770 163
Spokane WA 5 3 1.1 4343 870 81
Pierce WA 3 4 1.0 6269 730 105
Walla Walla WA 17 5 1.2 550 910 17
Yakima WA 2 6 0.9 10921 4380 53
Grays Harbor WA 25 7 1.4 116 160 3
Grant WA 9 8 1.0 1533 1620 28
Clark WA 8 13 0.9 2057 440 25
Snohomish WA 4 14 0.7 6170 780 41
Benton WA 6 15 0.8 3897 2010 29
Franklin WA 7 18 0.7 3626 4000 24
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.1 4905 610 72
Marion OR 3 2 1.1 2915 870 39
Washington OR 2 3 1.0 3083 530 40
Malheur OR 6 4 1.2 786 2580 17
Yamhill OR 10 5 1.1 464 450 15
Wasco OR 18 6 1.4 193 750 5
Clackamas OR 5 7 1.0 1543 380 20
Umatilla OR 4 8 0.9 2302 2990 37
Lane OR 8 10 1.1 587 160 11
Deschutes OR 7 11 1.0 604 330 11
Jackson OR 9 12 1.0 474 220 13
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Inyo CA 52 1 2.6 83 460 7
Los Angeles CA 1 2 1.0 208718 2070 2500
Mendocino CA 38 3 1.9 437 500 22
Del Norte CA 50 4 2.2 101 370 2
Riverside CA 2 5 1.1 41314 1730 507
San Diego CA 5 6 1.1 32723 990 440
Alameda CA 8 7 1.2 12923 790 218
Fresno CA 7 11 1.0 17532 1790 318
Kern CA 6 13 0.9 23162 2620 403
Orange CA 3 15 0.8 39581 1250 327
San Bernardino CA 4 16 0.8 35814 1680 372
San Joaquin CA 9 29 0.7 12537 1710 106

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.7 126616 2980 855
Pima AZ 2 2 1.0 17894 1750 201
Cochise AZ 11 3 1.2 1666 1320 24
Yuma AZ 3 4 0.7 11599 5580 66
Apache AZ 7 5 0.9 3187 4460 19
Coconino AZ 8 6 0.9 3094 2210 15
Mohave AZ 6 7 0.8 3207 1560 26
Navajo AZ 5 9 0.7 5384 4950 16
Santa Cruz AZ 9 12 0.7 2668 5730 8
Pinal AZ 4 13 0.3 8408 2000 20
CO
county ST case rank severity R_e cases cases/100k daily cases
San Miguel CO 32 1 2.0 89 1120 2
Pueblo CO 12 2 1.4 696 420 13
Broomfield CO 15 3 1.5 472 710 10
El Paso CO 4 4 1.0 5177 750 72
Summit CO 16 5 1.6 338 1110 2
Larimer CO 9 6 1.2 1550 460 24
Routt CO 26 7 1.5 125 500 3
Adams CO 3 8 0.9 6498 1310 62
Denver CO 1 10 0.9 10242 1480 66
Weld CO 6 11 1.1 3715 1260 23
Jefferson CO 5 12 0.9 4190 730 35
Arapahoe CO 2 13 0.8 7332 1150 46
Boulder CO 7 16 0.9 2082 650 19
Douglas CO 8 21 0.8 1741 530 13
UT
county ST case rank severity R_e cases cases/100k daily cases
Utah UT 2 1 1.0 8748 1480 117
Salt Lake UT 1 2 1.0 20702 1850 174
Wasatch UT 11 3 1.5 562 1840 5
Box Elder UT 12 4 1.3 372 700 7
Cache UT 6 5 1.2 1929 1580 14
Washington UT 5 6 1.0 2515 1570 26
Millard UT 13 7 1.5 135 1060 2
Davis UT 3 9 0.9 3230 950 34
Weber UT 4 10 0.9 2783 1120 29
Tooele UT 9 12 0.8 587 900 6
San Juan UT 8 14 0.7 653 4270 4
Summit UT 7 15 0.7 711 1760 2
NM
county ST case rank severity R_e cases cases/100k daily cases
Doña Ana NM 4 1 1.1 2463 1140 34
Eddy NM 14 2 1.4 300 520 8
Chaves NM 11 3 1.1 455 700 15
Lea NM 7 4 1.1 782 1120 19
Bernalillo NM 1 5 0.7 5144 760 39
Rio Arriba NM 13 6 1.2 320 810 3
Curry NM 10 7 0.9 555 1110 12
Santa Fe NM 9 8 0.9 650 440 9
San Juan NM 3 12 0.7 3047 2390 6
Sandoval NM 5 15 0.6 1136 810 6
McKinley NM 2 16 0.6 4050 5560 6
Otero NM 6 18 0.6 1104 1680 2
Cibola NM 8 19 0.3 685 2540 7

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Union NJ 6 1 1.7 16848 3050 17
Hudson NJ 3 2 1.1 19829 2970 24
Middlesex NJ 4 3 1.1 18117 2190 29
Gloucester NJ 16 4 1.1 3263 1120 23
Bergen NJ 1 5 1.0 21037 2260 37
Essex NJ 2 6 0.9 19975 2520 28
Camden NJ 9 7 0.9 8616 1700 30
Passaic NJ 5 9 0.9 17795 3530 22
Monmouth NJ 8 10 0.8 10409 1670 27
Ocean NJ 7 15 0.7 10673 1800 19
PA
county ST case rank severity R_e cases cases/100k daily cases
Northumberland PA 29 1 1.8 465 500 10
Huntingdon PA 37 2 1.9 307 680 3
York PA 13 3 1.3 2553 570 38
Mercer PA 30 4 1.5 434 390 15
Clearfield PA 42 5 1.6 170 210 6
Lycoming PA 32 6 1.4 388 340 9
Montour PA 53 7 1.7 103 560 1
Lancaster PA 6 9 1.0 5909 1100 43
Berks PA 7 10 1.1 5380 1290 27
Allegheny PA 4 13 0.8 8888 730 89
Montgomery PA 2 14 0.9 10113 1230 40
Philadelphia PA 1 19 0.7 31217 1980 84
Delaware PA 3 20 0.8 9258 1640 52
Lehigh PA 9 26 0.9 4959 1370 17
Bucks PA 5 27 0.8 7208 1150 30
Chester PA 8 35 0.7 5133 990 24
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore city MD 4 1 0.9 12560 2040 144
Baltimore MD 3 2 0.9 13276 1600 152
Montgomery MD 2 3 1.0 18397 1770 94
Prince George’s MD 1 4 0.9 24160 2670 137
Anne Arundel MD 5 5 0.9 7382 1300 61
Queen Anne’s MD 18 6 1.3 430 870 6
Howard MD 6 7 0.9 3855 1220 34
Charles MD 8 8 1.0 2028 1290 20
Harford MD 9 12 0.8 1973 790 22
Frederick MD 7 18 0.8 3074 1240 10
VA
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg VA 28 1 2.8 437 1420 27
Patrick VA 61 2 2.3 156 870 9
Westmoreland VA 49 3 2.3 210 1190 3
Essex VA 71 4 2.2 102 920 4
Richmond VA 37 5 2.5 322 3630 1
Carroll VA 36 6 2.1 332 1120 5
King George VA 64 7 2.1 143 550 4
Prince William VA 2 16 1.2 9480 2080 85
Fairfax VA 1 19 1.2 16377 1430 86
Loudoun VA 3 22 1.3 5272 1370 37
Virginia Beach city VA 4 29 1.0 4987 1110 100
Chesterfield VA 5 30 1.1 4381 1290 51
Norfolk city VA 7 42 0.9 3744 1520 68
Arlington VA 8 43 1.2 3081 1330 22
Henrico VA 6 47 0.9 3874 1190 37
Newport News city VA 9 48 1.0 1839 1020 28
WV
county ST case rank severity R_e cases cases/100k daily cases
Logan WV 9 1 1.5 231 680 16
Randolph WV 11 2 2.1 210 720 1
Grant WV 21 3 1.5 126 1080 9
Wayne WV 12 4 1.6 208 510 4
Raleigh WV 8 5 1.3 243 320 10
Fayette WV 18 6 1.5 147 330 3
Cabell WV 4 7 1.2 399 420 11
Kanawha WV 2 13 0.9 932 500 17
Berkeley WV 3 14 1.1 698 620 7
Wood WV 7 18 1.0 248 290 2
Ohio WV 6 22 0.6 270 630 2
Monongalia WV 1 23 0.4 944 900 3
Jefferson WV 5 24 0.7 296 530 1
DE
county ST case rank severity R_e cases cases/100k daily cases
Sussex DE 2 1 1.1 5850 2660 26
New Castle DE 1 2 0.9 7218 1300 45
Kent DE 3 3 1.0 2291 1310 13

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Clarke AL 38 1 2.7 657 2690 37
Washington AL 53 2 2.3 385 2310 16
Autauga AL 27 3 1.6 1177 2130 25
Montgomery AL 3 4 1.3 6881 3030 102
Jefferson AL 1 5 1.1 13429 2040 238
Mobile AL 2 6 0.9 10088 2430 181
Talladega AL 23 7 1.2 1289 1600 38
Tuscaloosa AL 5 10 1.0 4347 2110 56
Marshall AL 8 11 1.1 3253 3420 43
Shelby AL 7 16 1.0 3509 1660 51
Lee AL 9 17 1.0 2856 1790 35
Baldwin AL 6 20 0.9 3619 1740 58
Madison AL 4 24 0.8 5504 1540 68
MS
county ST case rank severity R_e cases cases/100k daily cases
Stone MS 77 1 1.8 199 1080 9
Harrison MS 3 2 1.2 2539 1250 64
Warren MS 16 3 1.4 1088 2310 23
Tishomingo MS 53 4 1.4 413 2120 18
Lee MS 10 5 1.2 1460 1720 39
Bolivar MS 14 6 1.2 1161 3560 34
Marshall MS 36 7 1.3 704 1970 22
DeSoto MS 2 12 0.9 3697 2100 60
Jackson MS 5 13 0.9 2318 1630 49
Washington MS 9 14 1.0 1669 3540 29
Forrest MS 8 21 1.0 1808 2390 28
Hinds MS 1 30 0.7 5652 2340 56
Jones MS 7 39 0.8 1904 2780 20
Madison MS 4 49 0.7 2435 2350 19
Rankin MS 6 50 0.7 2295 1520 22
LA
county ST case rank severity R_e cases cases/100k daily cases
West Feliciana LA 53 1 1.9 348 2260 4
Tensas LA 64 2 1.9 81 1740 4
Bienville LA 52 3 1.8 374 2740 4
Claiborne LA 59 4 1.7 266 1650 8
Lafayette LA 4 5 1.1 7749 3230 160
St. Martin LA 19 6 1.4 1680 3130 27
Red River LA 61 7 1.6 230 2670 7
East Baton Rouge LA 2 12 0.9 12204 2750 156
St. Tammany LA 7 13 1.0 5204 2060 68
Tangipahoa LA 9 14 1.0 3475 2660 50
Jefferson LA 1 15 0.8 15307 3520 113
Ouachita LA 8 16 1.0 4874 3120 58
Orleans LA 3 25 0.8 10726 2750 50
Caddo LA 6 29 0.8 6705 2700 59
Calcasieu LA 5 53 0.5 6858 3430 50

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Baker FL 51 1 3.1 845 3040 112
Dixie FL 57 2 3.1 521 3170 59
Franklin FL 62 3 2.4 449 3830 59
Calhoun FL 61 4 2.2 470 3250 33
Gulf FL 55 5 2.0 654 4070 57
Taylor FL 46 6 1.8 1069 4840 103
Escambia FL 12 7 1.5 9374 3010 248
Miami-Dade FL 1 13 0.8 132654 4890 1485
Duval FL 6 15 1.0 23420 2530 277
Hillsborough FL 4 16 0.9 32657 2370 372
Polk FL 9 17 1.0 14292 2140 208
Palm Beach FL 3 20 0.9 37018 2560 412
Broward FL 2 23 0.7 62356 3270 670
Orange FL 5 25 0.9 31690 2400 299
Pinellas FL 7 31 0.9 17896 1870 167
Lee FL 8 36 0.9 16533 2300 130
GA
county ST case rank severity R_e cases cases/100k daily cases
Bleckley GA 126 1 2.4 211 1650 16
Pulaski GA 148 2 2.1 108 960 5
Cherokee GA 12 3 1.4 3459 1430 100
Cobb GA 4 4 1.2 13596 1820 295
Warren GA 153 5 1.9 77 1440 4
Fulton GA 1 6 1.1 20296 1990 336
Ben Hill GA 78 7 1.7 429 2500 15
DeKalb GA 3 8 1.1 13926 1870 226
Gwinnett GA 2 10 1.0 19840 2200 320
Chatham GA 6 17 1.0 5746 2000 108
Muscogee GA 8 19 1.1 4738 2410 68
Hall GA 5 24 1.0 6106 3120 87
Clayton GA 7 27 1.0 5015 1800 74
Richmond GA 9 30 0.9 4338 2150 101

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Hamilton TX 179 1 3.5 84 1020 6
Medina TX 64 2 2.7 806 1630 45
Bee TX 54 3 2.4 1088 3330 113
Karnes TX 75 4 2.3 678 4410 72
Cherokee TX 53 5 2.0 1102 2120 63
Fort Bend TX 10 6 1.7 9734 1320 378
Winkler TX 177 7 2.5 86 1100 4
Tarrant TX 4 13 1.2 34031 1680 707
Nueces TX 9 14 1.3 14789 4100 385
Harris TX 1 16 0.9 86181 1870 1391
Hidalgo TX 6 21 1.1 19898 2340 337
El Paso TX 8 28 1.1 16325 1950 239
Dallas TX 2 29 0.9 54504 2110 501
Cameron TX 7 31 0.8 17390 4120 565
Travis TX 5 34 0.9 22849 1900 212
Bexar TX 3 61 0.6 42830 2220 236
OK
county ST case rank severity R_e cases cases/100k daily cases
Pittsburg OK 26 1 2.2 353 800 33
Love OK 51 2 2.2 77 780 2
Tulsa OK 2 3 1.0 10546 1640 213
Oklahoma OK 1 4 0.9 10635 1360 184
Choctaw OK 39 5 1.6 185 1240 4
Bryan OK 16 6 1.2 454 990 12
Washington OK 11 7 1.3 633 1220 9
Rogers OK 6 9 1.0 984 1080 26
Wagoner OK 7 12 1.0 866 1110 20
Texas OK 5 17 1.3 1057 5000 3
Cleveland OK 3 21 0.6 3029 1090 43
Canadian OK 4 24 0.8 1217 890 20
Comanche OK 9 36 0.8 821 670 7
McCurtain OK 8 39 0.8 857 2600 4

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Barry MI 38 1 2.2 181 300 2
Bay MI 21 2 1.8 627 600 13
Macomb MI 3 3 1.1 10678 1230 123
Oakland MI 2 4 1.0 15507 1240 109
Saginaw MI 8 5 1.3 2009 1040 25
Wayne MI 1 6 0.8 28234 1600 127
Menominee MI 43 7 1.4 137 590 6
Kent MI 4 8 0.9 7544 1170 50
Ottawa MI 9 16 0.9 1839 650 15
Washtenaw MI 6 19 0.8 3048 830 17
Genesee MI 5 20 0.8 3664 900 20
Jackson MI 7 47 0.3 2435 1530 2
WI
county ST case rank severity R_e cases cases/100k daily cases
Green WI 39 1 2.2 161 440 6
Lafayette WI 41 2 2.2 135 810 6
Oneida WI 44 3 1.9 129 360 8
Oconto WI 33 4 1.7 246 660 12
Iowa WI 50 5 1.9 81 340 3
Pierce WI 35 6 1.7 219 530 8
Portage WI 23 7 1.6 411 580 10
Milwaukee WI 1 9 1.0 21108 2210 204
Waukesha WI 4 13 1.1 4236 1060 94
Brown WI 3 15 1.1 4271 1640 41
Dane WI 2 17 1.0 4543 860 54
Racine WI 5 21 1.0 3532 1810 46
Outagamie WI 9 22 1.1 1255 680 24
Kenosha WI 6 30 0.9 2686 1600 29
Rock WI 7 33 1.0 1568 970 11
Walworth WI 8 40 0.7 1338 1300 14

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Jackson MN 55 1 2.4 83 830 3
McLeod MN 34 2 1.9 182 510 9
Hennepin MN 1 3 1.0 19270 1560 220
Pipestone MN 37 4 1.8 157 1710 3
Ramsey MN 2 5 1.1 7547 1390 106
Morrison MN 49 6 1.9 91 280 2
Dakota MN 3 7 1.1 4407 1050 73
Anoka MN 4 8 1.1 3672 1060 54
Washington MN 6 11 1.1 2118 840 36
Scott MN 9 12 1.1 1564 1090 30
Olmsted MN 8 14 1.1 1734 1130 18
Nobles MN 7 27 1.2 1764 8080 3
Stearns MN 5 29 0.9 2898 1850 11
SD
county ST case rank severity R_e cases cases/100k daily cases
Charles Mix SD 12 1 2.9 104 1110 1
Hughes SD 16 2 2.4 93 530 2
Yankton SD 10 3 2.2 114 500 2
Meade SD 14 4 1.7 95 350 4
Brookings SD 7 5 1.7 136 400 3
Roberts SD 18 6 1.7 81 790 2
Codington SD 8 7 1.6 133 480 2
Minnehaha SD 1 8 1.1 4414 2360 30
Brown SD 5 9 1.3 441 1140 6
Pennington SD 2 10 1.1 892 820 9
Beadle SD 4 11 1.5 592 3220 1
Lincoln SD 3 12 0.9 635 1160 10
Clay SD 9 13 1.0 128 920 2
Union SD 6 14 0.9 216 1420 3
ND
county ST case rank severity R_e cases cases/100k daily cases
Sioux ND 14 1 4.3 83 1880 8
Morton ND 4 2 1.7 364 1190 16
Burleigh ND 2 3 1.1 1173 1250 32
Stark ND 6 4 1.2 267 860 10
Cass ND 1 5 1.1 3039 1740 18
Williams ND 5 6 1.2 273 800 6
Grand Forks ND 3 7 1.2 678 960 7
Ward ND 7 8 1.2 224 320 6
Mountrail ND 9 9 1.1 131 1290 2
Benson ND 8 14 0.2 138 2000 2

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 0.6 13160 1530 11
Fairfield CT 1 2 0.4 17981 1900 16
Hartford CT 3 3 0.4 12782 1430 11
New London CT 5 4 0.7 1439 540 3
Windham CT 8 5 0.6 732 630 3
Middlesex CT 6 6 0.7 1400 860 2
Tolland CT 7 7 0.4 1064 700 3
Litchfield CT 4 8 0.4 1613 880 1
MA
county ST case rank severity R_e cases cases/100k daily cases
Essex MA 3 1 1.1 17846 2280 71
Suffolk MA 2 2 1.1 21856 2760 74
Middlesex MA 1 3 1.0 26420 1660 80
Norfolk MA 5 4 0.8 10639 1520 40
Bristol MA 6 5 0.9 9363 1680 31
Worcester MA 4 6 0.8 13634 1660 33
Plymouth MA 7 7 0.9 9248 1810 18
Hampden MA 8 8 0.9 7603 1620 20
Barnstable MA 9 10 0.8 1804 840 6
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 0.9 15129 2380 65
Kent RI 2 2 0.9 1507 920 10
Washington RI 3 3 0.9 611 480 2
Newport RI 4 4 0.9 400 480 2
Bristol RI 5 5 0.9 319 650 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.1 232531 2750 310
Chemung NY 41 2 1.9 172 200 1
Washington NY 32 3 2.1 259 420 1
Erie NY 7 4 1.1 8894 970 48
Niagara NY 14 5 1.4 1500 710 8
Suffolk NY 2 6 1.0 43805 2940 64
Allegany NY 51 7 1.8 80 170 1
Monroe NY 8 9 1.1 4955 670 28
Nassau NY 3 10 0.9 43674 3220 48
Westchester NY 4 11 1.0 36203 3740 31
Rockland NY 5 12 1.2 13943 4310 9
Orange NY 6 20 0.9 11174 2950 10
Dutchess NY 9 22 0.9 4615 1570 12

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.3 101 170 2
Chittenden VT 1 2 1.1 729 450 1
Bennington VT 5 3 1.3 87 240 0
Windham VT 3 4 1.2 103 240 0
Franklin VT 2 5 0.3 119 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Androscoggin ME 3 1 1.3 561 520 2
Cumberland ME 1 2 0.9 2088 720 5
York ME 2 3 0.6 675 330 2
Penobscot ME 5 4 0.6 154 100 1
Kennebec ME 4 5 0.4 172 140 0
NH
county ST case rank severity R_e cases cases/100k daily cases
Strafford NH 4 1 1.4 358 280 4
Rockingham NH 2 2 1.1 1694 560 8
Hillsborough NH 1 3 0.9 3852 940 12
Merrimack NH 3 4 1.0 465 310 1
Belknap NH 5 5 0.9 117 190 1
Cheshire NH 7 6 0.8 99 130 1
Carroll NH 8 7 0.7 95 200 1
Grafton NH 6 8 0.0 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 8816 2160 122
Abbeville SC 43 2 1.5 333 1350 7
York SC 9 3 1.1 3581 1380 58
Dorchester SC 11 4 1.1 3116 2000 50
Greenville SC 2 5 0.9 10932 2190 101
Spartanburg SC 8 6 1.1 4100 1360 51
Georgetown SC 17 7 1.1 1459 2370 28
Charleston SC 1 8 0.9 12334 3120 103
Horry SC 4 10 0.9 8614 2680 72
Beaufort SC 7 12 0.9 4128 2260 71
Berkeley SC 6 25 0.8 4174 2000 43
Lexington SC 5 26 0.8 4971 1740 44
NC
county ST case rank severity R_e cases cases/100k daily cases
Northampton NC 67 1 2.6 337 1670 14
Wilkes NC 43 2 2.0 814 1190 16
Sampson NC 24 3 1.8 1500 2360 15
Tyrrell NC 87 4 1.9 98 2380 1
Stanly NC 37 5 1.3 1070 1750 28
Mecklenburg NC 1 6 0.9 22287 2110 197
Wake NC 2 7 0.9 12085 1150 133
Guilford NC 4 9 1.0 5642 1080 67
Forsyth NC 5 10 1.1 5261 1420 53
Gaston NC 6 16 1.0 3341 1540 48
Union NC 8 18 1.0 3109 1370 44
Cumberland NC 9 20 0.9 3093 930 52
Durham NC 3 28 0.9 6171 2010 43
Johnston NC 7 43 0.8 3316 1730 38

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Yellowstone MT 1 1 1.1 1286 810 33
Flathead MT 4 2 1.2 338 340 13
Missoula MT 5 3 1.1 336 290 10
Big Horn MT 3 4 0.9 434 3240 14
Silver Bow MT 9 5 1.1 90 260 4
Ravalli MT 11 6 1.1 84 200 2
Lewis and Clark MT 8 7 0.9 162 240 4
Gallatin MT 2 8 0.7 945 900 9
Cascade MT 7 10 0.6 169 210 2
Lake MT 6 11 0.7 182 610 2
WY
county ST case rank severity R_e cases cases/100k daily cases
Washakie WY 13 1 2.4 75 920 5
Lincoln WY 9 2 1.4 104 550 2
Carbon WY 10 3 1.2 102 660 3
Park WY 7 4 1.0 136 470 2
Sheridan WY 12 5 1.0 75 250 2
Fremont WY 1 6 0.8 508 1270 4
Laramie WY 2 7 0.7 506 520 4
Uinta WY 4 8 0.7 279 1350 2
Campbell WY 8 9 0.9 124 260 1
Natrona WY 6 10 0.7 233 290 2
Sweetwater WY 5 11 0.6 261 590 2
Teton WY 3 12 0.4 377 1630 3
ID
county ST case rank severity R_e cases cases/100k daily cases
Nez Perce ID 21 1 2.0 158 390 5
Bonneville ID 5 2 1.5 1076 960 58
Canyon ID 2 3 1.2 5830 2750 143
Ada ID 1 4 1.1 8996 2020 142
Shoshone ID 23 5 1.6 101 810 6
Teton ID 24 6 1.6 89 800 4
Twin Falls ID 4 7 1.2 1405 1680 29
Kootenai ID 3 10 0.9 1815 1180 31
Jerome ID 9 15 1.0 479 2040 7
Minidoka ID 8 19 0.8 483 2340 6
Cassia ID 7 20 0.7 526 2230 6
Blaine ID 6 24 0.8 578 2630 1

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Belmont OH 29 1 2.5 620 910 6
Lawrence OH 45 2 1.8 285 470 12
Darke OH 37 3 1.5 401 780 13
Franklin OH 1 4 0.9 18348 1440 178
Cuyahoga OH 2 5 1.0 13532 1080 130
Hamilton OH 3 6 1.0 9647 1190 82
Logan OH 64 7 1.4 157 350 6
Mahoning OH 9 10 1.1 2552 1100 23
Summit OH 6 15 1.0 3568 660 44
Butler OH 8 16 1.0 2930 770 36
Montgomery OH 5 18 0.9 4355 820 53
Lucas OH 4 24 0.8 5375 1240 70
Marion OH 7 51 0.9 2930 4480 7
IL
county ST case rank severity R_e cases cases/100k daily cases
Jefferson IL 35 1 2.3 284 740 20
Jersey IL 58 2 2.3 108 490 8
LaSalle IL 17 3 1.7 769 700 43
Cook IL 1 4 1.1 111411 2130 723
Perry IL 45 5 1.9 175 820 11
Woodford IL 50 6 1.7 155 400 10
Franklin IL 43 7 1.7 176 450 9
DuPage IL 3 9 1.1 12248 1310 114
Will IL 5 11 1.1 9257 1340 95
Madison IL 9 13 1.2 2589 970 64
Lake IL 2 15 1.1 12687 1800 97
Kane IL 4 17 1.1 9827 1850 82
St. Clair IL 6 18 1.1 4422 1680 72
McHenry IL 8 31 0.9 3218 1050 32
Winnebago IL 7 49 0.8 3787 1320 15
IN
county ST case rank severity R_e cases cases/100k daily cases
Sullivan IN 71 1 2.7 125 600 10
Pulaski IN 79 2 2.4 84 660 2
Putnam IN 43 3 1.9 301 800 12
Carroll IN 57 4 1.9 191 960 10
Spencer IN 70 5 2.1 139 680 4
Vigo IN 30 6 1.6 649 600 32
Clinton IN 36 7 1.7 439 1360 14
Lake IN 2 9 1.3 7612 1560 81
Marion IN 1 10 1.1 16000 1690 172
St. Joseph IN 5 12 1.2 3496 1300 60
Allen IN 4 14 1.2 3909 1060 46
Hendricks IN 8 16 1.3 1910 1190 23
Hamilton IN 6 20 1.1 2786 880 44
Elkhart IN 3 26 1.1 4928 2420 40
Cass IN 9 27 1.4 1794 4710 7
Vanderburgh IN 7 30 1.1 1968 1090 40

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Overton TN 74 1 2.1 191 870 12
White TN 60 2 2.0 297 1120 18
Lake TN 25 3 1.8 817 10860 11
Weakley TN 51 4 1.6 457 1360 34
Jackson TN 80 5 2.0 126 1080 4
Greene TN 45 6 1.5 486 710 22
Morgan TN 82 7 1.6 122 560 8
Shelby TN 1 9 0.8 23577 2520 285
Davidson TN 2 10 0.9 22884 3350 198
Knox TN 5 17 0.9 4866 1070 110
Hamilton TN 4 18 1.0 6126 1710 78
Wilson TN 8 23 1.1 2294 1730 36
Sumner TN 7 26 1.1 3438 1920 42
Williamson TN 6 29 1.0 3544 1620 47
Rutherford TN 3 30 0.9 6548 2130 75
Montgomery TN 9 34 1.0 1941 990 37
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.2 8279 1080 185
Madison KY 14 2 1.5 503 560 20
Fayette KY 2 3 1.1 3900 1220 89
Carroll KY 42 4 1.8 159 1480 3
Washington KY 70 5 1.7 85 710 4
Hardin KY 11 6 1.4 619 570 16
Shelby KY 6 7 1.4 771 1650 11
Warren KY 3 16 1.0 2642 2090 30
Boone KY 5 22 1.0 1107 860 14
Kenton KY 4 26 0.9 1414 860 14
Daviess KY 7 27 0.9 769 770 9
Muhlenberg KY 8 40 1.1 647 2080 2
Christian KY 9 50 0.6 635 880 7

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Pulaski MO 35 1 2.0 212 400 6
Greene MO 6 2 1.4 1552 540 48
Christian MO 23 3 1.4 378 450 15
Clinton MO 61 4 1.7 83 410 3
Pike MO 55 5 1.6 101 550 5
St. Louis city MO 2 6 1.0 5224 1680 88
St. Louis MO 1 7 0.8 14929 1490 210
Jefferson MO 5 8 1.0 1811 810 47
Jackson MO 4 14 0.8 4042 580 80
Boone MO 7 15 1.0 1411 800 27
St. Charles MO 3 16 0.8 4150 1060 64
Jasper MO 8 35 0.8 1258 1060 8
Buchanan MO 9 48 0.7 1084 1220 3
AR
county ST case rank severity R_e cases cases/100k daily cases
Jackson AR 55 1 4.7 112 650 16
Clay AR 52 2 2.4 136 900 5
Poinsett AR 32 3 2.1 277 1150 22
Pulaski AR 2 4 1.3 5579 1420 111
Logan AR 34 5 1.5 267 1230 16
Craighead AR 8 6 1.3 1361 1290 37
Hot Spring AR 5 7 1.4 1537 4590 14
Sebastian AR 4 12 1.0 2198 1720 59
Jefferson AR 6 15 1.1 1537 2180 29
Crittenden AR 7 27 1.0 1370 2800 21
Benton AR 3 28 0.9 4780 1850 35
Washington AR 1 29 0.8 6312 2760 40
Pope AR 9 41 0.8 1321 2080 16

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 631.5 seconds to compute.
2020-08-10 11:07:15

version history

Today is 2020-08-10.
82 days ago: Multiple states.
74 days ago: \(R_e\) computation.
71 days ago: created color coding for \(R_e\) plots.
66 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
66 days ago: “persistence” time evolution.
59 days ago: “In control” mapping.
59 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
51 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
46 days ago: Added Per Capita US Map.
44 days ago: Deprecated national map.
40 days ago: added state “Hot 10” analysis.
35 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
33 days ago: added per capita disease and mortaility to state-level analysis.
21 days ago: changed to county boundaries on national map for per capita disease.
16 days ago: corrected factor of two error in death trend data.
12 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
7 days ago: added county level “baseline control” and \(R-e\) maps.
3 days ago: fixed normalization error on total disease stats plot.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.